SOTAVerified

Image Augmentation

Image Augmentation is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.

Source: Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing

( Image credit: Kornia )

Papers

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Showing 241–250 of 308 papers

TitleStatusHype
AugStatic - A Light-Weight Image Augmentation LibraryCode0
Adversarial Augmentation for Enhancing Classification of Mammography ImagesCode0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Parallel Grid Pooling for Data AugmentationCode0
Population Based Augmentation: Efficient Learning of Augmentation Policy SchedulesCode0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
Performance of GAN-based augmentation for deep learning COVID-19 image classificationCode0
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image CaptioningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified